Do Reductions in Search Costs for Partial Information on Online Platforms Lead to Better Consumer Decisions? Evidence of Cognitive Miser Behavior from a Natural Experiment

Dorothy Lianlian Jiang (), Shun Ye (), Liang Zhao () and Bin Gu ()
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Dorothy Lianlian Jiang: Decision and Information Science, C. T. Bauer College of Business, University of Houston, Houston, Texas 77004
Shun Ye: Information Systems and Operations Management, Costello College of Business George Mason University, Fairfax, Virginia 22030
Liang Zhao: Management, Marketing, and Information Systems, School of Business, Hong Kong Baptist University, Kowloon, Hong Kong SAR
Bin Gu: Department of Information Systems, Questrom School of Business Boston University, Boston, Massachusetts 02215

Information Systems Research, 2025, vol. 36, issue 3, 1780-1798

Abstract: Many online platforms have utilized information technology, such as artificial intelligence (AI), to reduce consumers’ information search costs and facilitate their decision-making processes. Given the variety of online information, these technologies are often effective in reducing the search cost for only specific information types: a concept we refer to as search cost reduction for partial information. For rational consumers, this can lead to improved decision making. However, consumers do not always behave rationally and may exhibit behavioral biases in their decision-making process. In this study, we propose that search cost reduction for partial information can induce cognitive miser behavior in consumers, ultimately leading to worse decision-making. To explore this understudied puzzle, we leverage a natural experiment on Yelp to examine the effect of enabling search cost reduction for partial information on the quality of consumers’ decisions regarding restaurants. We constructed a unique panel data set using matched pairs of restaurants across Yelp and TripAdvisor. By applying a difference-in-differences design, we aim to casually infer how consumer decision quality is affected following the introduction of Yelp’s new AI-powered image categorization feature in August 2015, which was designed to reduce the search cost of review images. We find that adding the AI-powered image categorization feature has a negative effect on consumer decision quality. Delving into the text analysis of consumer complaints using deep learning techniques, we further find that the inferior decision quality of consumers postfeature introduction is primarily due to reduced awareness of restaurants’ service quality—information that is readily available in review texts but not in review images. Our findings suggest that reducing search costs for partial information may hurt consumers as it may incentivize cognitive miser behavior. This occurs as consumers disproportionately pay attention to the product information of which the search cost has been reduced, whereas paying less attention to other relevant product information. We discuss the implications of these findings for online platforms.

Keywords: search cost reduction; online platforms; AI-powered image categorization feature; decision-making; deep learning techniques; difference-in-differences (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:36:y:2025:i:3:p:1780-1798

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